Abstract
-
The recent few years have witnessed a rapid surge of par-
ticipatory web and social media, enabling a new laboratory
for studying human relations and collective behavior on an
unprecedented scale. In this work, we attempt to harness
the predictive power of social connections to determine the
preferences or behaviors of individuals such as whether a
user supports a certain political view, whether one likes one
product, whether he/she would like to vote for a presidential
candidate, etc. Since an actor is likely to participate in mul-
tiple dierent communities with each regulating the actor's
behavior in varying degrees, and a natural hierarchy might
exist between these communities, we propose to zoom into
a network at multiple dierent resolutions and determine
which communities are informative of a targeted behavior.
We develop an ecient algorithm to extract a hierarchy of
overlapping communities. Empirical results on several large-
scale social media networks demonstrate the superiority of
our proposed approach over existing ones without consider-
ing the multi-resolution or overlapping property, indicating
its highly promising potential in real-world applications